A multi-level matching algorithm for combining similarity measures in ontology integration

Authors:
Ahmed Alasoud;Volker Haarslev;Nematollaah Shiri
Affiliations:
Computer Science & Software Engineering, Concordia University, Montreal, Quebec, Canada;Computer Science & Software Engineering, Concordia University, Montreal, Quebec, Canada;Computer Science & Software Engineering, Concordia University, Montreal, Quebec, Canada
Venue:
ODBIS'05/06 Proceedings of the First and Second VLDB conference on Ontologies-based databases and information systems
Year:
2005

Citing 10
Cited 2

Clustering algorithms

Information retrieval
SEMINT: a tool for identifying attribute correspondences in heterogeneous databases using neural networks

Data & Knowledge Engineering
Learning to map between ontologies on the semantic web

Proceedings of the 11th international conference on World Wide Web
Generic Schema Matching with Cupid

Proceedings of the 27th International Conference on Very Large Data Bases
Comparison of Schema Matching Evaluations

Revised Papers from the NODe 2002 Web and Database-Related Workshops on Web, Web-Services, and Database Systems
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching

ICDE '02 Proceedings of the 18th International Conference on Data Engineering
The description logic handbook: theory, implementation, and applications

The description logic handbook: theory, implementation, and applications
Logics for Emerging Applications of Databases

Logics for Emerging Applications of Databases
The PROMPT suite: interactive tools for ontology merging and mapping

International Journal of Human-Computer Studies
An algebraic framework for schema matching

WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management

An empirical comparison of ontology matching techniques

Journal of Information Science
An effective ontology matching technique

ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Various similarity measures have been proposed for ontology integration to identify and suggest possible matches of components in a semi-automatic process. A (basic) Multi Match Algorithm (MMA) can be used to combine these measures effectively, thus making it easier for users in such applications to identify "ideal" matches found. We propose a multi-level extension of MMA, called MLMA, which assumes the collection of similarity measures are partitioned by the user, and that there is a partial order on the partitions, also defined by the user. We have developed a running prototype of the proposed multi level method and illustrate how our method yields improved match results compared to the basic MMA. While our objective in this study has been to develop tools and techniques to support the hybrid approach we introduced earlier for ontology integration, the ideas can be applied in information sharing and ontology integration applications.